{"workflow":{"id":13209,"name":"Generate competitor-style image ads from your product photos with Gemini","views":80,"recentViews":2,"totalViews":80,"createdAt":"2026-02-04T08:59:13.333Z","description":"# Image Ad Cloner AI Agent \n\nThis n8n workflow automates “cloning” a competitor ad style and re-generating it using your product image. A user uploads your product image and provides a competitor ad URL via a form trigger; the workflow downloads both images, converts them to inline/base64 parts, builds a detailed generation prompt, sends the request to a generative-vision model (Gemini-style endpoints), waits for the result, converts the output back to a file and returns it. Use-cases: rapid ad iteration, creative A/B variants, and scaled ad re-skins.\n\n---\n\n# How it works (step-by-step)\n1. `form_trigger` — user submits: competitor image URL, their product file, and optional changes.  \n2. `convert_product_image_to_base64` — converts uploaded product file to base64 inline data.  \n3. `download_image` — fetches the competitor ad image from the provided URL.  \n4. `convert_ad_image_to_base64` — converts downloaded competitor image to base64 inline data.  \n5. `build_prompt` — assembles a careful prompt (handles partial text, labels, placements, CTA swaps, additional changes).  \n6. `generate_ad_image_prompt` → `generate_ad_image` — two HTTP Request nodes call generative endpoints (first to prepare prompt/content and then flash-image generation). These include inline image data and model-specific parameters.  \n7. `Wait` — allows async generation to complete (workflow uses a wait/webhook pattern).  \n8. `set_result` → `get_image` — extract image result, convert binary to file for downstream use (download or return to the requester).  \n9. `Sticky Note` — visual diagram / docs inside workflow.  \n\n---\n\n## Quick Setup Guide\n👉 [Demo & Setup Video](https://drive.google.com/file/d/1PLFPHSXQ-DaNAKeBQv7G9SzaXT0c3hRt/view?usp=sharing)\n👉 [Course](https://www.udemy.com/course/n8n-automation-mastery-build-ai-powered-enterprise-ready/?referralCode=2EAE71591D3BEB80F2CC)\n\n---\n\n# Nodes of interest\n- `form_trigger` (n8n-nodes-base.formTrigger) — entry point; validates inputs.  \n- `convert_product_image_to_base64` & `convert_ad_image_to_base64` (extractFromFile) — convert uploaded/downloaded binaries to inline/base64 data for the model.  \n- `download_image` (httpRequest) — fetches competitor image URL. Add robust URL validation.  \n- `build_prompt` (set) — builds the natural-language prompt that instructs the model how to replace branding, packaging, and CTAs.  \n- `generate_ad_image_prompt` & `generate_ad_image` (httpRequest) — POST to generative APIs (examples use Gemini endpoints). Have retry/backoff settings.  \n- `Wait` — used to allow model generation to complete before extracting results.  \n- `set_result` / `get_image` / `convertToFile` — assemble the final binary file to deliver to the user.\n\n---\n\n# What you’ll need (credentials & permissions)\n- n8n account with Workflow active toggle & webhook exposure (or self-hosted endpoint).  \n- HTTP credential(s) for your generative model provider:\n  - **Header auth** credential for any required custom headers (`httpHeaderAuth`).  \n  - **Bearer token** credential for the model API (`httpBearerAuth`).  \n  - (Optional) Generic HTTP auth if using proxies or intermediate APIs.  \n- Storage (optional): S3 or other if you want to persist generated images.  \n- (Recommended) An OCR or moderation service API key if you plan to detect/remove copyrighted logos or PII.  \n- Ensure your hosting domain allows outbound HTTPS to the model endpoints and that webhook URLs are reachable.\n\n---\n\n# Recommended settings & best practices\n- **Input validation**: enforce allowed mime types (jpg, png, webp), max file size (e.g., 5–10 MB), and sanitize competitor URL domain whitelist.  \n- **Retries & backoff**: use `retryOnFail` and `waitBetweenTries` for `httpRequest` nodes (example uses `retryOnFail: true` and `waitBetweenTries: 5000`). Limit retries to avoid abuse.  \n- **Rate limits & quotas**: guard calls to the model API with rate-limiting / queueing to avoid hitting provider quotas.  \n- **Ethics & copyright check**: run OCR/logo-detect and flag requests that attempt to reproduce trademarked content; require human approval before creating ads that replicate competitor branding.  \n- **Partial-text handling**: include logic in the prompt (and optionally OCR pre-check) to identify fragment text on packaging and replace it correctly with your brand text.  \n- **Binary handling**: always set and preserve correct `mime_type` in inline data (png/jpeg) to avoid corrupt outputs.  \n- **Webhooks & timeouts**: set a sensible Wait time and webhook timeout; provide progress/error messages back to the requester.  \n- **Logging & monitoring**: capture model responses and node errors to troubleshoot prompt issues and image artifacts. Obfuscate tokens in logs.  \n- **Security**: store API keys in n8n credentials (never as plain text in Set nodes). Use least privilege.\n\n---\n\n# Customization ideas\n- Add an **OCR node** (e.g., Tesseract or cloud OCR) prior to `build_prompt` to detect and list text to replace automatically.  \n- Add a **logo mask** step to blur or remove competitor logos before feeding to the generator.  \n- Generate **multiple variants** per request (different CTAs, color palettes) and save them for A/B testing.  \n- Add human-in-the-loop: store generated images in a review queue and send approval emails/webhooks before final download.  \n- Integrate **analytics**: tag generated ads and push metadata (prompt, model used, timestamp) to a database for later analysis.  \n- Replace Gemini endpoints with other providers or allow a **model selector** UI option.  \n- Add automatic compliance moderation filters and a “safe mode” toggle that refuses disallowed transformations.\n\n---\n\n# Tags \nimage-ad-cloner, n8n, gemini, generative-ai, image-editing, ad-cloning, automation, form-trigger, viral\n","workflow":{"id":"dVdQ40xm6xUuMToE","meta":{"instanceId":"4764540811cbb8d2e1efff249fe019f120efdfc1d1ad5d0e40382bf91ca0b3dc","templateCredsSetupCompleted":true},"name":"Image Ad Cloner AI Agent","tags":[],"nodes":[{"id":"cde8e8f1-fa9a-4d9a-b090-1e58d2f7c8fd","name":"form_trigger","type":"n8n-nodes-base.formTrigger","position":[-800,-432],"webhookId":"739f6e4b-c69f-4baf-b6fe-f671f8ba6279","parameters":{"options":{},"formTitle":"Facebook Ad Thief Form","formFields":{"values":[{"fieldLabel":"Competitor Image Link","placeholder":"https://www.facebook.com/","requiredField":true},{"fieldType":"file","fieldLabel":"Your Product Image","multipleFiles":false,"requiredField":true},{"fieldLabel":"Additional changes"}]},"formDescription":"Upload your product image and submit a competitor image url for the ads you want to clone/spin for your 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\"mime_type\": \"image/jpeg\",\n          \"data\": \"{{ $node['convert_ad_image_to_base64'].json.data }}\"\n        }\n      }\n    ]\n  }]\n}","sendBody":true,"specifyBody":"json","authentication":"genericCredentialType","genericAuthType":"httpHeaderAuth"},"credentials":{"httpBearerAuth":{"id":"3EULvRek47J5GTvc","name":"Gemini"},"httpHeaderAuth":{"id":"VBnXim8Eh0tKnSvl","name":"Header Auth account 2"}},"retryOnFail":true,"typeVersion":4.2,"waitBetweenTries":5000},{"id":"c99b2abb-e1f9-4133-9d8b-0ce22a8ce310","name":"generate_ad_image","type":"n8n-nodes-base.httpRequest","position":[544,-432],"parameters":{"url":"https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash-image:generateContent","method":"POST","options":{},"jsonBody":"={\n  \"contents\": [{\n    \"parts\": [\n      { \"text\": {{ JSON.stringify($json.candidates.first().content.parts.first().text) }} },\n      {\n        \"inline_data\": {\n          \"mime_type\": \"image/png\",\n          \"data\": \"{{ $node['convert_product_image_to_base64'].json.data }}\"\n        }\n      },\n      {\n        \"inline_data\": {\n          \"mime_type\": \"image/jpeg\",\n          \"data\": \"{{ $node['convert_ad_image_to_base64'].json.data }}\"\n        }\n      }\n    ]\n  }]\n}","sendBody":true,"specifyBody":"json","authentication":"genericCredentialType","genericAuthType":"httpHeaderAuth"},"credentials":{"httpBearerAuth":{"id":"3EULvRek47J5GTvc","name":"Gemini"},"httpHeaderAuth":{"id":"VBnXim8Eh0tKnSvl","name":"Header Auth account 2"}},"retryOnFail":true,"typeVersion":4.2,"waitBetweenTries":5000},{"id":"63bc2fec-26c3-47cc-86df-026f831b9fda","name":"build_prompt","type":"n8n-nodes-base.set","position":[96,-432],"parameters":{"options":{},"assignments":{"assignments":[{"id":"2fcf84d7-4a17-4899-9bea-a7d3b6027acd","name":"prompt","type":"string","value":"=You are an expert in image I need your help to write a perfect prompt that is going to help me generate/edit an image for my company's product. Attached to this request are two images: 1. Product picture  (our product) 2. Reference image of the competitor's ad we're trying to replicate In your analysis of these images, I want you to come up with a plan and write a very detailed prompt on how we can clone and spin the ad from image number two (our competitor) to make an identical ad that features our product with our packaging  and image number one. We need to replicate the same style used on the  competitor ad, but it should always be our packaging that's included. Some tricky edge cases you need to handle are: Partial branding items of the text (there can be text that says , labels on products that say , multiple products, multiple packages that are different from ours). Please check for all placements in the  ad that need to be replaced. You need to figure out how to write a specific prompt that is going to detail this image generator how to replace it and create the ad for our product. The end result we're going for is a new ad that promotes our product . Make sure your prompt includes changes that need to be changed on the text of the image like labels and calls to action. It's possible that the source ad image for our competitor  will have partial text on the product or packaging like \"A\", \"AG\", \"G, \"G\" (standalone), \"G1\", or some other partial text that needs to be checked for and analyzed in your plan. Keep the ad copy the same EXCEPT for switching competitor brand name to our name to Think deeply about the prompt you are going to write. Also consider any additional changes requested   such as {{ $('form_trigger').item.json['Additional changes'] }}"}]}},"typeVersion":3.4},{"id":"c6a71cd4-b74f-4ef2-a098-c82907594c8c","name":"Sticky Note","type":"n8n-nodes-base.stickyNote","position":[-1520,-528],"parameters":{"width":694,"height":688,"content":"# Image Ad Cloner AI Agent \n\nThis n8n workflow automates “cloning” a competitor ad style and re-generating it using your product image. A user uploads your product image and provides a competitor ad URL via a form trigger; the workflow downloads both images, converts them to inline/base64 parts, builds a detailed generation prompt, sends the request to a generative-vision model (Gemini-style endpoints), waits for the result, converts the output back to a file and returns it. Use-cases: rapid ad iteration, creative A/B variants, and scaled ad re-skins.\n\n\n## How it works (step-by-step)\n1. `form_trigger` — user submits: competitor image URL, their product file, and optional changes.  \n2. `convert_product_image_to_base64` — converts uploaded product file to base64 inline data.  \n3. `download_image` — fetches the competitor ad image from the provided URL.  \n4. `convert_ad_image_to_base64` — converts downloaded competitor image to base64 inline data.  \n5. `build_prompt` — assembles a careful prompt (handles partial text, labels, placements, CTA swaps, additional changes).  \n6. `generate_ad_image_prompt` → `generate_ad_image` — two HTTP Request nodes call generative endpoints (first to prepare prompt/content and then flash-image generation). These include inline image data and model-specific parameters.  \n7. `Wait` — allows async generation to complete (workflow uses a wait/webhook pattern).  \n8. `set_result` → `get_image` — extract image result, convert binary to file for downstream use (download or return to the requester).  \n9. `Sticky Note` — visual diagram / docs inside workflow.  \n\n\n## Quick Setup Guide\n👉 [Demo & Setup Video](https://drive.google.com/file/d/1PLFPHSXQ-DaNAKeBQv7G9SzaXT0c3hRt/view?usp=sharing)\n👉 [Course](https://www.udemy.com/course/n8n-automation-mastery-build-ai-powered-enterprise-ready/?referralCode=2EAE71591D3BEB80F2CC)"},"typeVersion":1},{"id":"fa8c0a9a-ba60-49d7-acf0-a41e54cc995c","name":"Wait","type":"n8n-nodes-base.wait","position":[768,-432],"webhookId":"27f36994-15a0-48bd-a4d3-078a8a4ad633","parameters":{},"typeVersion":1.1},{"id":"4bf93051-3d43-478c-967e-5fb79d333a24","name":"Sticky Note1","type":"n8n-nodes-base.stickyNote","position":[-800,-528],"parameters":{"color":6,"width":838,"height":352,"content":"## 📥  INPUT & IMAGE PREPARATION\nThis section collects the required inputs and prepares images for AI processing.\n"},"typeVersion":1},{"id":"670f8a87-d6f5-4a93-9f0a-01690ff4e008","name":"Sticky Note2","type":"n8n-nodes-base.stickyNote","position":[64,-528],"parameters":{"color":6,"width":646,"height":352,"content":"## 🧠  PROMPT ENGINEERING & AI GENERATION\nThis section constructs the instructions and generates the new advertisement.\n\n"},"typeVersion":1},{"id":"f36ee66f-204f-4fc5-899c-f3edd232610e","name":"Sticky Note3","type":"n8n-nodes-base.stickyNote","position":[736,-528],"parameters":{"color":6,"width":678,"height":352,"content":"## 📦 RESULT PROCESSING & OUTPUT\nThis section processes the generated image and prepares the final result.\n\n"},"typeVersion":1}],"active":false,"pinData":{},"settings":{"executionOrder":"v1"},"versionId":"9203cce4-25bf-4ef0-8a54-ec4ffa1a1106","connections":{"Wait":{"main":[[{"node":"set_result","type":"main","index":0}]]},"get_image":{"main":[[]]},"set_result":{"main":[[{"node":"get_image","type":"main","index":0}]]},"build_prompt":{"main":[[{"node":"generate_ad_image_prompt","type":"main","index":0}]]},"form_trigger":{"main":[[{"node":"convert_product_image_to_base64","type":"main","index":0}]]},"download_image":{"main":[[{"node":"convert_ad_image_to_base64","type":"main","index":0}]]},"generate_ad_image":{"main":[[{"node":"Wait","type":"main","index":0}]]},"generate_ad_image_prompt":{"main":[[{"node":"generate_ad_image","type":"main","index":0}]]},"convert_ad_image_to_base64":{"main":[[{"node":"build_prompt","type":"main","index":0}]]},"convert_product_image_to_base64":{"main":[[{"node":"download_image","type":"main","index":0}]]}}},"lastUpdatedBy":1,"workflowInfo":{"nodeCount":14,"nodeTypes":{"n8n-nodes-base.set":{"count":2},"n8n-nodes-base.wait":{"count":1},"n8n-nodes-base.stickyNote":{"count":4},"n8n-nodes-base.formTrigger":{"count":1},"n8n-nodes-base.httpRequest":{"count":3},"n8n-nodes-base.convertToFile":{"count":1},"n8n-nodes-base.extractFromFile":{"count":2}}},"status":"published","readyToDemo":null,"user":{"name":"Pratyush Kumar Jha","username":"pratyush","bio":"Automation consultant specialising in n8n workflows. 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